PubMedCentral-PMC5418659.pdf

Multiethnic Exome-Wide Association Study of Subclinical

Atherosclerosis

A full list of authors and affiliations appears at the end of the article.

Abstract

Background—The burden of subclinical atherosclerosis in asymptomatic individuals is heritable and associated with elevated risk of developing clinical coronary heart disease (CHD). We sought to identify genetic variants in protein-coding regions associated with subclinical atherosclerosis and the risk of subsequent CHD.

Methods and Results—We studied a total of 25,109 European ancestry and African-American participants with coronary artery calcification (CAC) measured by cardiac computed tomography and 52,869 with common carotid intima media thickness (CIMT) measured by ultrasonography within the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium. Participants were genotyped for 247,870 DNA sequence variants (231,539 in exons) across the genome. A meta-analysis of exome-wide association studies was performed across cohorts for CAC and CIMT. APOB p.Arg3527Gln was associated with four-fold excess CAC (P =

3×10-10). The APOE ε2 allele (p.Arg176Cys) was associated with both 22.3% reduced CAC (P =

1×10-12) and 1.4% reduced CIMT (P = 4×10-14) in carriers compared with non-carriers. In

secondary analyses conditioning on LDL cholesterol concentration, the ε2 protective association

with CAC, although attenuated, remained strongly significant. Additionally, the presence of ε2

was associated with reduced risk for CHD (OR 0.77; P = 1×10-11).

Conclusions—Exome-wide association meta-analysis demonstrates that protein-coding variants

in APOB and APOE associate with subclinical atherosclerosis. APOE ε2 represents the first

significant association for multiple subclinical atherosclerosis traits across multiple ethnicities as well as clinical CHD.

Keywords

Genome Wide Association Study; exome; coronary artery calcification; carotid intima-media thickness; genomics

Correspondence: Christopher J. O'Donnell, MD MPH, Associate Professor Medicine, Harvard Medical School, Chief of Cardiology, Boston Veterans Affairs Healthcare System, 1400 Veterans of Foreign Wars Parkway, Building 1 5B-113, Boston, MA 02132, Tel: 857-203-6840, Fax: 857-203-5550, [email protected].

*contributed equally

All other authors did not report any relevant disclosures.

HHS Public Access

Author manuscript

Circ Cardiovasc Genet

Published in final edited form as:

Circ Cardiovasc Genet. 2016 December ; 9(6): 511–520. doi:10.1161/CIRCGENETICS.116.001572.

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Background

Coronary heart disease (CHD) remains the leading cause of death and infirmity in developed

countries.1 Atherosclerosis is the underlying pathology of CHD.2 The presence of

atherosclerosis in individuals without clinical CHD, termed “subclinical atherosclerosis,” is associated with increased risk of developing clinical CHD independent of traditional risk

factors prior to the onset of symptoms.3-6 Subclinical atherosclerosis is a heritable7-9 clinical

phenotype that can be ascertained non-invasively as coronary artery calcification (CAC) by cardiac computed tomography (CT) and common carotid intima media thickness (CIMT) by

carotid ultrasound.10

Genome-wide association studies (GWAS) within the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium have discovered sites of common

non-coding genetic variation associated with both CAC11, 12 and CIMT7, 11 among those of

European ancestry. Non-coding single nucleotide polymorphisms (SNPs) at the 9p21 and 6p24 regions, near the CDKN2A and PHACTR1 genes, respectively, are strongly associated

with both CAC burden and myocardial infarction (MI).12 The 8q24 (ZHX2), 19q13

(APOC1), and 8q23 (PINX1) loci are strongly associated with CIMT.7 Observed

associations for subclinical atherosclerosis among individuals of European ancestry,

however, have not been replicated in those of African ancestry.13, 14 Furthermore, since the

biologic implications of non-coding variation are not as readily interpreted as with coding

variation, the roles of such variants in human atherosclerosis remain unclear.15

Protein-coding variation tends to be infrequently observed and is often inadequately catalogued on

earlier GWAS arrays.16 Rare genomic variation is not well-imputed and exome sequencing

to detect such uncommon variation across large populations remains a costly endeavor. Here, we leverage the Illumina HumanExome BeadChip array, enriched for protein-coding

variation.17 We investigated whether there is evidence for associations of protein-coding

variation with two measures of subclinical atherosclerosis across individuals of European and of African ancestry. And we further determine whether such DNA sequence variations may influence CHD risk.

Methods

Study Populations

The Illumina HumanExome Beadchip v1.0 or v1.1 (also known as the “exome chip”) was used to genotype participants across 19 cohorts of the CHARGE Consortium

(Supplement).18 Participants with a diagnosis of CHD at the time of CAC phenotyping were

excluded from CAC analysis. Participants who underwent carotid endarterectomy prior to CIMT phenotyping were excluded from CIMT analysis. 25,109 participants had CAC measured and 52,869 participants had CIMT measured. Each study received institutional review board approval, participants provided written informed consent, and respective governing ethics committees approved each study.

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Measures

CAC Measurement—Cohorts used different CT scanners to ascertain CAC scoring (Table S1). CAC scoring by multidetector CT and by electron beam CT have been previously described to be highly concordant and are both recognized as valid tools to estimate CAC

score.19-21 Total CAC score was quantified by the sum of CAC area weighted by density

within individual coronary arteries by the Agatston method and the continuous score was

used for analysis.22

CIMT Measurement—Common carotid intima media thickness was derived by bilateral longitudinal common carotid artery analysis (imaging and measurement methods are described in the Table S2). The mean of the maximum thickness for each common carotid artery was the analytical variable.

Statistical Analyses

According to prespecified analysis plans, association analyses and meta-analyses were

performed using the seqMeta package (http://cran.r-project.org/web/packages/seqMeta/

index.html) in the R statistical software as has previously been performed for exome

chip-based analyses.23 To reduce skewness, CAC was natural log transformed after adding 1 and

CIMT was natural log transformed. Each cohort performed an analysis for each genomic variant with the trait of interest independently and separately for individuals of European and African ancestry to minimize population biases. Covariates in the models included age,

sex, and principal components of ancestry derived using EIGENSTRAT.24 For studies with

related samples, the pairwise kinship matrix was computed and accounted for in the regression model. Score statistics and genotypic covariance matrices were computed for each cohort and used for additive single variant and gene-based analyses, respectively.

For our primary analyses, we tested the association of each genomic variant with CAC and with CIMT across all samples by meta-analysis that included all cohorts, irrespective of ancestry. We performed single variant analyses on variants that had a minor allele count of at least 20 and gene-based analyses for genes with combined minor allele frequency (MAF) of nonsynonymous variants at least 0.2% to reduce the likelihood of false positive results. We also performed two gene-based tests: 1) T1, where nonsynonymous variants with minor allele frequency (MAF) <1% were collapsed into a gene-based statistic, and 2) sequence kernel association test (SKAT) with MAF <5% for nonsynonymous variants to better account for collapsed variants with bidirectional phenotypic consequences. Regional

association plots were generated using LocusZoom.25 For our secondary analyses, we tested

the association of each genomic variant with CAC and CIMT by meta-analysis separately among cohorts of European and African ancestry.

Given the 238,065 variants on the array that passed quality control, the Bonferroni-adjusted

level of significance for single variant tests was 0.05/238,065 = 2.10×10-7. Given the 17,574

genes with nonsynonymous variants on the array, the Bonferroni-adjusted level of

significance for gene-based tests was 0.05/17,574 = 2.85×10-6. For CAC, we had >90%

power to detect a variant (MAF <1%) with effect size 0.31 standard deviations, or a gene (combined MAF <1%) with effect size 0.28 standard deviations at a sample size of 25,000.

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For CIMT, we had >90% power to detect a variant (MAF <1%) with effect size 0.21 standard deviations or a gene (combined MAF <1%) with effect size 0.20 standard deviations with a sample size of 52,000. Power calculations were performed using the

Genetic Power Calculator.26

Methods for the secondary analyses are presented in the Supplement.

Results

Study Participants

19 cohorts participated in the meta-analyses of these two subclinical atherosclerotic traits and the clinical characteristics are summarized in Table S1 and Table S2. A total of 25,109 participants were genotyped with the array and had CAC assessed; of these participants, 19,980 were of European ancestry and 5,129 were of African ancestry. 52,869 participants were genotyped and had CIMT assessed; 44,963 were of European ancestry and 7,906 were of African ancestry. 222,701 (93.5%) of the 238,065 variants were polymorphic in the CAC meta-analysis; of polymorphic variants, 193,373 (97.1%) were annotated as nonsynonymous or splice-site variants. Similarly, 227,344 (95.5%) of array variants were polymorphic in the CIMT meta-analysis and, of these, 217,235 (95.6%) were nonsynonymous or splice-site variants.

Coronary Artery Calcification Association

Figure 1 plots the meta-analysis CAC association P-value by genomic locus for each variant. The top loci with lead variants associated with CAC among all participants are listed in Table 1. No systematic association inflation was observed across the set of statistical tests performed (Figure S1).

We identified previously-described common non-coding variant associations at the 9p21 and 6p24 loci. A 9p21 haplotype marked by lead SNP rs10757278-G (MAF 43%), an intergenic variant, was replicated and associated with increased CAC quantity (23.4%; 95% CI: 18.6,

28.3%; P = 2×10-24). Similarly, rs9349379-G (MAF 34%), an intronic variant within

PHACTR1, was associated with increased CAC quantity (20.9%; 95% CI: 16.3, 25.8 %; P =

5×10-20). While these associations were robust for those of European ancestry, there was no

apparent evidence for association in those of African ancestry (Figure S2, Figure S3). Both loci display locus heterogeneity, or multiple independent associations, for CAC in those of European ancestry (Table 1). We did not discover non-coding variants at other loci on the exome chip that met our stringent Bonferroni alpha threshold. Previously, rs3809346, an

intronic variant of COL4A2, had a suggestive association with CAC,12 but now in our

European ancestry sample size that is twice as large, genome-wide significant association

was not observed (P = 2×10-3).

Among functional variants, a nonsynonymous APOB (rs5742904-T; MAF 0.2%; NM_000384.2:c.10580G>A; NP_000375.2:p.Arg3527Gln) variant was significantly associated with CAC quantity. Carriers of the rare APOB missense variant had markedly

increased CAC (4.1-fold; 95% CI: 2.6-, 6.4-fold; P = 3×10-10). In our meta-analysis, the Old

Order Amish cohort primarily accounted for the strong association, and the variant was

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extremely rarely observed within other cohorts. Furthermore, the variant was not seen among individuals of African ancestry (Figure S4). We also discovered a distinct rare APOB missense variant (rs1801696-T; MAF 0.6% European ancestry; NM_000384.2:c.7696G>A; NP_000375.2:p.Glu2566Lys), detected in individuals of European ancestry in most cohorts, that was moderately associated with increased CAC (1.9-fold; 95% CI: 1.6-, 2.1-fold; P =

9×10-6). This variant was not observed in individuals of African ancestry.

Additionally, a missense 19q13 variant within the APOE gene (rs7412-T; MAF 7.4% European ancestry, 10.8% African ancestry; NM_000041.2:c.526C>T;

NP_000032.1:p.Arg176Cys) was associated with diminished CAC quantity (-22.3%; 95%

CI, -27.6- -16.7%; P = 1×10-12) (Figure S5). This association was consistent in those of both

European ancestry (-17.3%; 95% CI: -23.7, -10.3%; P = 4×10-6) and African ancestry

(-35.2%; 95% CI: -43.6, -25.7%; P = 5×10-10) without significant heterogeneity (P = 0.53)

(Figure 2). Additionally, an independent variant (rs769449-A; MAF 11% European ancestry, 2.4% African ancestry) within an intron of APOE also had nominal evidence of association with increased CAC quantity only in individuals of European ancestry (+15.0%; 95% CI:

7.9, 22.6%; P = 2×10-5).

To improve power of discovery for rare protein-coding variants, we conducted gene-based analyses by aggregating such variants within a gene into a single statistical unit to increase the exposure rate. However, collapsing nonsynonymous variants on the exome chip within a gene did not yield genome-wide significant results (Figure S6).

Carotid Intima Media Thickness Association

There was no systematic inflation of CIMT associations with any variant (Figure S7). The top meta-analysis association findings are listed in Table 2 and a Manhattan plot of all associations is presented in Figure 3.

We noted that, in addition to diminished CAC, the rs7412-T APOE ε2 allele was associated

with diminished CIMT (-1.4%; 95% CI: -1.8, -1.0%; P = 4×10-14). There was consistency of

association across European and African ancestry cohorts (Figure 4 and Figure S8). There was no significant heterogeneity among the cohorts for this association (P heterogeneity = 0.23)

There were two additional independent suggestive associations at 19q13 at non-coding variants. A variant 5kb upstream of LDLR (rs11668477) was associated with diminished

CIMT (P = 5×10-7) primarily among those of European ancestry. This variant has previously

been associated with reduced LDL cholesterol.27 The nearby rs7188-G variant (MAF 33%

European ancestry, 7.9% African ancestry) within the 3′UTR region of KANK2 was

associated with CIMT in those of European ancestry (P = 1×10-6). Additionally, a rare

missense variant (rs143873045-A; MAF 0.5% African ancestry; NM_001136191.2:c. 1274C>T; NP_001129663.1:p.Ser425Leu) in KANK2 only observed in individuals of

African ancestry showed suggestive association with increased CIMT (P = 4×10-4). Lastly,

in gene-based analyses, collapsing nonsynonymous variants within a gene did not yield significant associations (Figure S9).

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APOE ε2's Effect Conditional on LDL Cholesterol

We sought to determine whether LDL cholesterol concentration accounted for the observed

ε2 association with CAC. First, when restricting the original analysis only to participants

with LDL cholesterol measurements (n = 20,527), ε2 remained significantly associated with

reduced CAC quantity (-22.3%; 95% CI: -25.1, -19.3%; P = 2×10-11) (Table S3). When

further adjusting for medication-adjusted LDL cholesterol, the effect estimate was diminished yet the association remained genome-wide significant (-17.0%; 95% CI: -19.7,

-14.2%; P = 2×10-8).

APOE ε2's Effect Conditional on ε3 and ε4

Given the absence of ε4 from the array, we sought to determine whether ε2's apparent effect

on reduced CAC quantity was due to a referent that includes a previously described risk

allele (ε3 + ε4). 5,872 participants had CAC and the major APOE genotypes assessed by

PCR. Each APOE genotype's association with CAC (to the ε3/ε3 referent) was performed

by cohort and ethnicity and subsequently meta-analyzed with fixed effects. ε2/ε3 was

associated with 10.8% reduced CAC (95% CI: -19.6, -0.01%; P = 0.03) and ε2/ε2 with

27.4 % reduced CAC (95% CI: -45.2, -0.04%; P = 0.03) (Figure S10).

Concordance of CHD Variants with Subclinical Atherosclerosis Associations

Of the 57 loci previously associated with CHD mainly in individuals of European or South Asian descent, 40 published variants were on the array and available for analysis. 32 of the

40 variants have the same effect direction for CAC and CHD (P = 1.8×10-4) whereas only 23

variants were concordant for CIMT (P = 0.43) in European ancestry participants (Table S4). When restricting the analysis to variants with at least nominal association (P < 0.05) with

CAC, all 17 had concordant effect directions (P = 4.8×10-7). A similar analysis with variants

at least nominally associated with CIMT showed that 6 of 11 had concordant effect directions for CHD (P = 0.56).

Replication of Convergent Subclinical Atherosclerosis Finding with CHD

21,182 individuals of European ancestry, independent of the sample for subclinical atherosclerosis investigations, were genotyped by the Illumina HumanExome BeadChip

array, of whom 9,472 had CHD.28 In cross-sectional analyses, meta-analysis of rs7412-T

confirmed a significantly lower odds of CHD (odds ratio 0.77; 95% CI: 0.71, 0.84; P =

1.47×10-10).

Discussion

In our exome-wide association analysis for subclinical atherosclerosis in two distinct ethnicities, we find that protein-coding mutations in APOB and APOE are associated with subclinical atherosclerosis. While the association for APOB was driven by a founder

mutation in the Amish, a missense mutation in APOE (ε2) was associated with both reduced

CAC and CIMT in individuals of European ancestry and African ancestry, even when

adjusting for LDL cholesterol concentration. Furthermore, carriers of the ε2 allele had a

reduced risk of coronary heart disease. Here, we provide evidence for the first exome-wide

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association across multiple subclinical atherosclerosis traits and multiple ethnicities for

APOE ε2.

Both CAC and CIMT have been proposed as proximal clinical phenotypes of atherosclerosis that may identify individuals at high risk for developing clinical CHD. However, we see that alleles that associate with increased CHD risk also appear to largely result in increased CAC, which is less consistently observed with CIMT. This is concordant with the prior observation

that CAC outperforms CIMT in predicting cardiovascular events.5, 29 Recently, post hoc

analyses in statin trials to prevent cardiovascular disease observed that those with a higher burden of CHD-predisposing alleles are more likely to derive clinical benefit from

preventive statin therapy.30

The APOB p.Arg3527Gln (also known as p.Arg3500Gln) has been previously been shown

to lead to increased concentrations of LDL cholesterol and premature CHD.31 Our

association signal for this variant was nearly exclusively driven by the Old Order Amish, where it is known to be a founder mutation (MAF 12%) predisposing to increased LDL cholesterol concentrations and CAC quantity through disruption of the LDL receptor

binding domain.32 We also observed a distinct APOB missense mutation, p.Glu2566Lys,

with borderline association with increased CAC quantity. Unlike p.Arg3527Gln,

p.Glu2566Lys does not occur within the LDL receptor binding domain but occurs within a

conserved amphipathic motif of the β2 domain predicted to influence the conversion of

VLDL to LDL.33

Furthermore, we demonstrated that APOE p.Arg176Cys (ε2 allele) was associated with

reduced CAC and reduced CIMT in both individuals of European and African ancestry. APOE is an essential mediator of the catabolism and clearance of triglyceride-rich and

cholesterol-rich lipoproteins. The major alleles, ε2, ε3, and ε4, have been previously linked

to cardiovascular disease, from the candidate gene era, and ε2 is the least common

allele.34, 35 Previously, CHD risk predisposition from ε4 was primarily thought to be

mediated by LDL cholesterol raising effects but observations with ε2 have been mixed.35

Similarly, ε4, unlike ε2, has been generally linked to ischemic stroke risk.36 Major reasons

for the lack of association of the major APOE alleles with cardiovascular traits in prior genome-wide association studies include the notable absence of rs7412 and rs429358 on population-based genotyping arrays as well as poor imputation of these variants. Similarly, rs429358 is not included on the array used for this study.

ApoE is a major ligand of LDL receptor and a key mediator of remnant lipoprotein particle

clearance.37, 38 The ε2 allele is believed to result in less efficient LDL receptor binding by

altering the positive potential.39 Using publicly available data, ε2 does not impact

expression of nearby genes in GTEx nor does it demonstrate enhancer or promoter

chromatin marks in ENCODE HepG2 liver cells supporting ε2's direct impact on ApoE

itself. ApoE ε2 can alternatively clear lipoproteins via cell-surface heparan sulfate

proteoglycan and LDL receptor-related protein.40-42 ApoE ε2 transgenic mice crossbred

with ApoB transgenic mice have lower LDL cholesterol.42 Furthermore, ApoE ε2 transgenic

mice lacking LDL receptor still had lower LDL cholesterol suggesting that

hypocholesterolemia appears independent of ε2's effects on LDL receptor.35, 43 ApoE ε2

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impairs lipoprotein lipase-mediated metabolism of VLDL to LDL potentially through the

displacement of ApoCII, an activator of lipoprotein lipase.43 The consequent diminished

hepatic cholesterol may subsequently increase LDL receptors for ApoB-containing lipoproteins like LDL.

Interestingly, despite accounting for LDL cholesterol or serum triglycerides, we observe that

ε2 still is highly associated with reduced CAC quantity. It is likely that single cross-sectional

measure of lipoproteins, while correlates with, does not fully account for lifelong lipoprotein

exposures. ApoE ε2 homozygotes who develop type III hyperlipoproteinemia have a marked

increase in remnant lipoprotein particles unlike heterozygotes. Analogously, ApoE ε

2-overexpressing mice have increased hepatic VLDL production.42 Thus, while ApoE ε2

heterozygotes may have an increase in VLDL production and decreased triglyceride catabolism via lipoprotein lipase, the observation of similar triglyceride levels compared to non-carriers suggests preservation of, or enhanced, clearance of remnant lipoprotein

particles. We hypothesize that ApoE ε2's association with reduced subclinical

atherosclerosis may be due to increased clearance of both atherogenic LDL and remnant lipoprotein particles through LDL receptor-dependent and -independent pathways. Further work is needed to test this hypothesis.

Our study has several strengths. First, we perform a genetic association meta-analysis across the largest set of individuals to-date for subclinical atherosclerosis in two distinct ancestries. Second, we characterize the association of protein-coding genomic variation, which has not been well studied at the population level, with subclinical atherosclerosis. Third, we explore mechanisms of association through lipoprotein-mediation analyses. Fourth, we provide novel insights with both cross-ethnicity and cross-atherosclerosis trait observations. Fifth, we relate the associations of these subclinical atherosclerosis genetic variants on risk for CHD.

While our study has several strengths, we note some key limitations. First, not all protein-coding variation is catalogued on the exome chip. Due to purifying selection, disruptive

protein-coding variation is rare.44 By potentially not accounting for the totality of disruptive

variation not on the array, variance is increased and power is not optimized for gene-based analyses. Whole exome sequencing can better address this limitation as such technologies continue to become more cost-effective for large-scale experiments. Second, our analyses of prior associations at non-coding sites are restricted to sites on the exome chip. We were able

to robustly replicate prior non-coding association analyses for CAC at 9p21 and 6p24.12 A

prior meta-analysis for CIMT genome-wide association discovered one genome-wide

association, an intergenic common variant (rs11781551-A) 385kb from ZHX2 at 8q24.7 No

variant with modest linkage disequilibrium with this variant was present on the exome chip thereby limiting ability for replication. An intronic variant in PINX1 at 8q23 and intergenic variant 2.3kb from APOC1 at 19q13 previously had suggestive association but no suitable proxies to replicate association were available on the exome chip. Third, our analysis still demonstrates a paucity of genome-wide associations for these quantitative atherosclerotic traits and highlights an important challenge to ongoing CAC association analyses.

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Genetic determinants of CHD have been characterized among individuals of European ancestry but the strongest association signals have not replicated in those of African ancestry which may be due to smaller sample sizes hindering statistical power or different key genetic drivers. But now we demonstrate a cardioprotective genetic mechanism in those of European ancestry and African ancestry through the reduction of subclinical atherosclerosis. We

propose potential mechanisms and call for renewed attention to APOE ε2 in the genesis of

atherosclerosis underlying clinical cardiovascular disease. Lastly, given the strong

concordance of subclinical atherosclerosis measures and clinical CHD, our findings support a future study of genotypes, subclinical atherosclerosis, and incident CHD.

Supplementary Material

Refer to Web version on PubMed Central for supplementary material.

Authors

Pradeep Natarajan, MD, MMSc1,2,*, Joshua C. Bis, PhD3,*, Lawrence F. Bielak, DDS, MPH4, Amanda J. Cox, PhD5, Marcus Dörr, MD6,7, Mary F. Feitosa, PhD8, Nora Franceschini, MD, MPH9, Xiuqing Guo, PhD10, Shih-Jen Hwang, PhD11, Aaron Isaacs, PhD12,13, Min A Jhun, PhD4, Maryam Kavousi, MD, PhD12, Ruifang Li-Gao, MSc14, Leo-Pekka Lyytikäinen, MD15,16, Riccardo E. Marioni, PhD17,18,19, Ulf Schminke, MD20, Nathan O. Stitziel, MD, PhD8,21,22, Hayato Tada, MD23, Jessica van Setten, PhD24, Albert V. Smith, PhD25,26, Dina Vojinovic, MD, MSc12, Lisa R. Yanek, MPH27, Jie Yao, MD, MS10, Laura M. Yerges-Armstrong, PhD28, Najaf Amin, PhD12, Usman Baber, MD29, Ingrid B. Borecki, PhD30, J. Jeffrey Carr, MD, MSc31, Yii-Der Ida Chen, PhD10, L. Adrienne Cupples, PhD32, Pim A. de Jong, MD, PhD33, Harry de Koning, PhD34, Bob D. de Vos, MSc35, Ayse Demirkan, PhD12, Valentin Fuster, MD, PhD29, Oscar H. Franco, MD, PhD12, Mark O. Goodarzi, MD, PhD36, Tamara B. Harris, MD37, Susan R. Heckbert, MD, PhD38, Gerardo Heiss, MD, PhD9, Udo Hoffmann, MD, PhD39, Albert Hofman, MD, PhD12, Ivana Išgum, PhD35, J. Wouter Jukema, MD, PhD40,41,42, Mika Kähönen, MD PhD43,44, Sharon L.R. Kardia, PhD4, Brian G. Kral, MD, MPH27, Lenore J. Launer, PhD37, Joe Massaro, PhD32, Roxana Mehran, MD29, Braxton D. Mitchell, PhD, MPH28,45, Thomas H. Mosley Jr., PhD46, Renée de Mutsert, PhD14, Anne B. Newman, MD47, Khanh-dung Nguyen, PhD48, Kari E. North, PhD9, Jeffrey R. O'Connell, PhD28, Matthijs Oudkerk, MD49, James S. Pankow, PhD, MPH50, Gina M. Peloso, PhD1,2,32, Wendy Post, MD, MS27, Michael A. Province, PhD8, Laura M. Raffield, PhD5, Olli T. Raitakari, MD, PhD51,52, Dermot F. Reilly, PhD48, Fernando Rivadeneira, MD, PhD12,53,54, Frits Rosendaal, MD, PhD14, Samantha Sartori, PhD29, Kent D. Taylor, PhD10, Alexander Teumer, PhD7,55, Stella Trompet, PhD40, Stephen T. Turner, MD56, Andre G. Uitterlinden, PhD12,53,54, Dhananjay Vaidya, PhD, MPH, MBBS27, Aad van der Lugt, MD, PhD57, Uwe Völker, PhD7,58, Joanna M. Wardlaw, MD17,59, Christina L. Wassel, PhD, MS60, Stefan Weiss, PhD7,58, Mary K. Wojczynski, PhD8, Diane M. Becker, ScD, PhD27, Lewis C. Becker, MD27, Eric Boerwinkle, PhD61,62, Donald W. Bowden, PhD5, Ian J. Deary, PhD17,63, Abbas Dehghan, MD, PhD12, Stephan B. Felix, MD6,7, Vilmundur Gudnason, MD,

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PhD25,26, Terho Lehtimäki, MD, PhD15,16, Rasika Mathias, ScD27, Dennis O. Mook-Kanamori, MD, PhD14,64, Bruce M. Psaty, MD3,38,65,66, Daniel J. Rader, MD67, Jerome I. Rotter, MD10, James G. Wilson, MD68, Cornelia M. van Duijn, PhD12,54, Henry Völzke, MD7,55,69, Sekar Kathiresan, MD1,2, Patricia A. Peyser, PhD4, Christopher J. O'Donnell, MD, MPH70, and CHARGE Consortium

Affiliations

1_{Ctr for Human Genetic Rsrch & Cardiovascular Rsrch Ctr, Mass Gen Hospital,}

Boston 2_{Program in Medical & Population Genetics, Broad Inst of Harvard & MIT,} Cambridge, MA 3_{Cardiovascular Hlth Rsrch Unit, Depts. of Medicine, Univ of} Washington, Seattle, WA 4_{Dept of Epidemiology, School of Public Hlth, Univ of} Michigan, Ann Arbor, MI 5_{Diabetes Heart Study, Wake Forest Hlth Sciences,} Winston-Salem, NC 6_{Depts. of Internal Medicine, Univ Medicine Greifswald,} Greifswald 7_{DZHK (German Ctr for Cardiovascular Rsrch), partner Site Greifswald,} Germany 8_{Division of Statistical Genetics, Dept of Genetics, Washington Univ} School of Medicine, St. Louis, MO 9_{Epidemiology, Gilling School of Global Public} Hlth, Univ of North Carolina, Chapel Hill, NC 10_{Inst for Translational Genomics &} Population Sciences, Los Angeles Biomedical Rsrch Inst & Dept of Pediatrics at Harbor-UCLA Medical Ctr, Torrance, CA 11_{Framingham Heart Study, National Heart} Lung & Blood Inst / National Insts of Hlth, Framingham, MA 12_{Genetic Epidemiology} Unit, Depts. of Epidemiology, Univ Medical Ctr Rotterdam, Rotterdam 13_CARIM School for Cardiovascular Diseases, Maastricht Ctr for Systems Biology, Dept of Biochemistry, Maastricht Univ, Maastricht 14_{Depts. of Clinical Epidemiology, Leiden} Univ Medical Ctr, Leiden, Netherlands 15_{Dept of Clinical Chemistry, Fimlab}

Laboratories 16_{Depts of Clinical Chemistry, Univ of Tampere School of Medicine,} Tampere, Finland 17_{Ctr for Cognitive Ageing & Cognitive Epidemiology, Univ of} Edinburgh, Edinburgh, United Kingdom 18_{Ctr for Genomic & Experimental Medicine,} Inst of Genetics & Molecular Medicine, Univ of Edinburgh, Edinburgh, United Kingdom 19_{Queensland Brain Inst, Univ of Queensland, Brisbane, QLD, Australia} 20_{Neurology, Univ Medicine Greifswald, Greifswald} 21_{Cardiovascular Division, Dept}

of Medicine, Washington Univ School of Medicine, St. Louis, MO 22_McDonnell Genome Inst, Washington Univ School of Medicine, St. Louis, MO 23_{Division of} Cardiovascular Medicine, Kanazawa Univ Graduate School of Medicine, Kanazawa, Japan 24_{Cardiology, Univ Medical Ctr Utrecht, Utrecht, Netherlands} 25_Icelandic Heart Association, Kopavogur 26_{Faculty of Medicine, Univ of Iceland, Reykjavik,} Iceland 27_{Dept of Medicine, Johns Hopkins Univ School of Medicine} 28_{Division of} Endocrinology, Diabetes & Nutrition, Dept of Medicine, Univ of Maryland School of Medicine, Baltimore, MD 29_{Cardiovascular Inst, Icahn School of Medicine at Mount} Sinai, New York, NY 30_{Analytical Genetics, Regeneron Pharmaceuticals Inc.,} Tarrytown, NY 31_{Radiology, Cardiovascular Medicine & Biomedical Informatics,} Vanderbilt, Nashville, TN 32_{Dept of Biostatistics, Boston Univ School of Public Hlth,} Boston, MA 33_{Dept of Radiology, Univ Medical Ctr Utrecht, Utrecht, Netherlands} 34_{Dept of Public Hlth, Erasmus Medical Ctr Rotterdam, Rotterdam, Netherlands} 35_{Image Sciences Inst, Univ Medical Ctr Utrecht, Utrecht, Netherlands} 36_{Division of}

Endocrinology, Diabetes & Metabolism, Cedars-Sinai Medical Ctr, Los Angeles, CA

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37_{Laboratory of Epidemiology & Population Sciences, National Inst on Aging}

Intramural Rsrch / National Insts of Hlth, Baltimore, MD 38_{Epidemiology, Univ of} Washington, Seattle, WA 39_{Dept of Radiology, Mass Gen Hospital, Boston} 40_{Cardiology, Leiden Univ Medical Ctr, Leiden, Netherlands} 41_{Durrer Ctr for}

Cardiogenetic Rsrch, Netherlands Heart Inst, Amsterdam 42_{InterUniv Cardiology} Inst of the Netherlands, Netherlands Heart Inst, Utrecht, Netherlands 43_{Dept of} Clinical Physiology, Tampere Univ Hospital, Tampere, Finland 44_{Clinical Physiology,} Univ of Tampere School of Medicine, Tampere, Finland 45_{Geriatrics Rsrch &} Education Clinical Ctr, Baltimore Veterans Administration Medical Ctr, Baltimore, MD 46_{Division of Geriatric Medicine, Depts. of Medicine, Univ of Mississippi Medical} Ctr, Jackson, MS 47_{Dept of Epidemiology, Graduate School of Public Hlth, Univ of} Pittsburgh, Pittsburgh, PA 48_{Genetics & Pharmacogenomics Dept, Merck Sharpe &} Dohme Corp., Boston, MA 49_{Ctr for Medical Imaging - North East Netherlands, Univ} Medical Ctr Groningen, Univ of Groningen, Groningen, Netherlands 50_{Division of} Epidemiology & Community Hlth, Univ of Minnesota, Minneapolis, MN 51_{Dept of} Clinical Physiology & Nuclear Medicine, Turku Univ Hospital 52_{Rsrch Ctr of Applied} & Preventive Cardiovascular Medicine, Univ of Turku, Turku, Finland 53_Internal Medicine, Univ Medical Ctr Rotterdam, Rotterdam 54_{Netherlands Consortium for} Hlthy Aging, Rotterdam, Netherlands 55_{Inst for Community Medicine, Univ Medicine} Greifswald, Greifswald 56_{Division of Nephrology & Hypertension, Dept of Internal} Medicine, Mayo Clinic, Rochester, MN 57_{Radiology, Erasmus MC, Univ Medical Ctr} Rotterdam, Rotterdam 58_{Interfaculty Inst of Genetics & Functional Genomics, Univ} Medicine Greifswald, Greifswald 59_{Division of Neuroimaging Sciences & Brain Rsrch} Imaging Ctr, Ctr for Clinical Brain Sciences, Univ of Edinburgh, Edinburgh, United Kingdom 60_{Dept of Pathology & Laboratory Medicine, The Univ of Vermont,} Colchester, VT 61_{Human Genetics Ctr, The Univ of Texas Hlth Science Ctr at}

Houston, Houston, TX 62_{Epidemiology, School of Public Hlth, The Univ of Texas Hlth} Science Ctr at Houston, Houston, TX 63_{Dept of Psychology, Univ of Edinburgh,} Edinburgh, United Kingdom 64_{Public Hlth & Primary Care, Leiden Univ Medical Ctr,} Leiden, Netherlands 65_{Hlth Services, Univ of Washington, Seattle, WA} 66_{Group Hlth} Rsrch Institute, Group Hlth Cooperative, Seattle, WA 67_{Division of Translational} Medicine & Human Genetics, Dept of Medicine, Perelman School of Medicine, Univ of Pennsylvania, Philadelphia, PA 68_{Physiology & Biophysics, Univ of Mississippi} Medical Ctr, Jackson, MS 69_{German Ctr for Diabetes Rsrch (DZD), Greifswald,} Germany 70_{Division of Cardiology, Boston Veterans Affairs Hlthcare System,} Boston, MA

Acknowledgments

Sources of Funding: P.N. is supported by the John S. LaDue Memorial Fellowship in Cardiology, Harvard Medical

School. M.K. is supported by the NWO VENI grant (VENI, 91616079). Infrastructure for the CHARGE Consortium is supported in part by the National Heart, Lung, and Blood Institute grant R01HL105756. Funding support for the CHARGE Consortium Exome Chip analyses is provided in part by the National Heart, Lung, and Blood Institute grant R01HL120393. Please refer to the Supplementary Materials regarding additional sources of funding.

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Disclosures: P.N. reports grant support from Amarin Corporation. I.B.B. is the Executive Director of Analytical

Genetics at Regeneron Pharmaceuticals Inc. Ivana Isgum is reports research grants from Pie Medical Imaging BV and the Netherlands Organisation for Health Research and Development (ZonMw). Oscar H. Franco O.H. is employed by ErasmusAGE, a center for aging research across the life course funded by Nestlé Nutrition (Nestec Ltd.), Metagenics Inc., and AXA. Ingrid B. Borecki is employed by Regeneron Pharmaceuticals Inc. Dermot F. Reilly is employed by Merck. Khanh-dung Nguyen is employed by Biogen.

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33. Segrest JP, Jones MK, De Loof H, Dashti N. Structure of apolipoprotein B-100 in low density lipoproteins. J Lipid Res. 2001; 42:1346–1367. [PubMed: 11518754]

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36. Sudlow C, Martinez Gonzalez NA, Kim J, Clark C. Does apolipoprotein E genotype influence the risk of ischemic stroke, intracerebral hemorrhage, or subarachnoid hemorrhage? Systematic review and meta-analyses of 31 studies among 5961 cases and 17,965 controls. Stroke. 2006; 37:364–370. [PubMed: 16385096]

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44. MacArthur DG, Balasubramanian S, Frankish A, Huang N, Morris J, Walter K, et al. A systematic survey of loss-of-function variants in human protein-coding genes. Science. 2012; 335:823–828. [PubMed: 22344438]

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Clinical Perspective

Atherosclerosis is the underlying pathologic substrate for coronary heart disease. The presence of atherosclerosis, termed “subclinical atherosclerosis”, is associated with an increased risk of developing clinical coronary heart disease. Genetic factors influence the development of subclinical atherosclerosis. Prior analyses of single nucleotide variants (SNVs) across the genome, have identified SNVs associated with both coronary artery calcification (CAC) and carotid intima media thickness (CIMT). However, such variants reside within non-coding portions of the genome limiting the interpretation of the biological role of these SNVs. Therefore, we employed a novel genotyping platform focused on densely cataloguing protein-coding SNVs across the genome. We associated these protein-coding SNVs with CAC and CIMT in 25,109 and 52,869 individuals, respectively, of European or African ancestry. We discovered that APOE p.Arg176Cys

(APOE ε2 allele) is associated with reduced CAC and CIMT in carriers compared to

non-carriers. For the first time, we observed these associations in both individuals of European ancestry and of African ancestry. The association of the variant with CAC is preserved even after accounting for its effects on LDL cholesterol. Finally, carriers are at reduced risk for developing clinical coronary heart disease. These observations represent novel insights about the genetic determinants of atherosclerosis in a multiethnic sample.

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Figure 1.

Association of each genotyped variant with CAC quantity. Plot of -log10(P) for association

of genotyped variants by chromosomal position for all autosomal polymorphisms analyzed in the age-, sex-, and principal components- adjusted model of coronary artery calcification quantity in the meta-analysis. The genes associated with the top associated variants are displayed.

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Figure 2.

Forest plot of relative CAC quantity for APOE ε2 carriers. CAC quantity for APOE ε2

carriers relative to non-carriers is displayed for all cohorts stratified by European and African ancestries to demonstrate consistency across diverse cohorts and ethnicities.

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Figure 3.

Association of each genotyped variant with CIMT. Plot of -log10(P) for association of

genotyped variants by chromosomal position for all autosomal polymorphisms analyzed in the age-, sex-, and principal components- adjusted model of carotid intima media thickness in the meta-analysis. The genes associated with the top associated variants are displayed.

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Figure 4.

Forest plot of relative CIMT for APOE ε2 carriers. CIMT for APOE ε2 carriers relative to

non-carriers is displayed for all cohorts stratified by European and African ancestries to demonstrate consistency across diverse cohorts and ethnicities.

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op meta-analysis v

ariant associations f

or cor

onary artery calcif

ication quantity All EA AA V ariant Consequence Near est Gene * Chr om:P os † Minor Allele MAF Beta ‡ SE P MAF P MAF P rs10757278 § inter genic (CDKN2B ) 9:22124477 G 0.43 0.21 0.020 3.14×10 -24 0.48 2.9×10 -25 0.21 0.20 rs9349379 ‖ intronic PHA CTR1 6:12903957 G 0.34 0.19 0.020 4.93×10 -20 0.39 1.28×10 -19 0.094 0.088 rs7412 # missense APOE 19:45412079 T 0.081 -0.25 0.036 1.19×10 -12 0.074 4.43×10 -6 0.11 5.36×10 -10 rs1412829 § intronic CDKN2B 9:22043926 C 0.34 -0.14 0.021 1.56×10 -11 0.41 5.58×10 -12 0.072 0.84 rs5742904 ** missense APOB 2:21229160 T 2.1×10 -3 1.41 0.22 2.93×10 -10 2.7×10 -3 2.93×10 -10 0 N A rs9369640 ‖ intronic PHA CTR1 6:12901441 A 0.43 -0.11 0.019 4.91×10 -8 0.38 5.04×10 -9 0.36 0.71 rs769449 # intronic APOE 19:45410002 A 0.10 0.14 0.032 7.93×10 -6 0.11 1.86×10 -6 0.024 0.19 rs1801696 ** missense APOB 2:21232044 T 4.6×10 -3 0.63 0.14 1.44×10 -5 5.7×10 -3 9.77×10 -6 0 N A

* Genes for SNPs that are outside the transcript boundary of the protein-coding gene are sho

wn in parentheses [e

g, (

CDKN2B

)].

† Genomic positions correspond to GRCh37.p13 reference, forw

ard strand.

‡ β

-Coef

ficients are estimated for natural log transformation of total Ag

atston CA

C score+1.

§ CDKN2B

lead v

ariants sho

w modest correlation among EA (r

2=0.24) and no correlation among AA.

‖ PHA

CTR1

lead v

ariants sho

w modest correlation among EA (r

2=0.36) and AA (r 2=0.05)

# APOE

lead v

ariants sho

w minimal correlation among EA (r

2=0.01) and no correlation among AA.

** APOB

lead v

ariants are not observ

ed to be correlated.

Abbre

viations: AA=African ancestry; AF=minor allele frequenc

y; Chrom:Pos=hg19 b

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op meta-analysis v

ariant associations f

or car

otid intima media thickness

All EA AA V ariant Consequence Near est Gene * Chr om:P os † Minor Allele MAF Beta ‡ SE P MAF P MAF P rs7412 missense APOE 19: 45412079 T 0.083 -0.014 0.0022 3.79×10 -14 0.079 1.97×10 -10 0.11 1.43×10 -5 rs11668477 inter genic (LDLR ) 19:11195030 G 0.27 -0.0064 0.0016 4.69×10 -7 0.20 5.26×10 -6 0.34 0.030 rs7188 3 ′ UTR (KANK2 ) 19:11275139 G 0.29 0.0054 0.0011 2.23×10 -6 0.33 1.36×10 -6 0.079 0.98 rs1712790 inter genic (F AM55B ) 11:114621469 C 0.47 -0.0048 0.0011 5.93×10 -6 0.48 1.89×10 -6 0.21 0.89 rs2298375 missense C22orf15 22:24106448 A 0.086 0.0082 0.0019 9.51×10 -6 0.085 5.64×10 -6 0.091 0.061 rs174547 intronic (F ADS1 ) 11:61570783 C 0.30 -0.0049 0.0011 1.07×10 -5 0.34 3.84×10 -5 0.082 0.062

* Genes for SNPs that are outside the transcript boundary of the protein-coding gene are sho

wn in parentheses [e

g, (

LDLR

)].

† Genomic positions correspond to GRCh37.p13 reference, forw

ard strand.

‡ β

-Coef

ficients are estimated for natural log transformation of CIMT

.

Abbre

viations: AA=African ancestry; AF=minor allele frequenc

y; Chrom:Pos=hg19 b

uild chromosome:position; EA=European ancestry; SE=standard error; UTR=untranslated re